Future Climate Predictions Research Articles

A statistical bias correction technique for global climate model predicted near-surface temperature in India using the generalized regression neural network

Abstract Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network – a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951–2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006–2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.

The Seasonal Snowfall Contributions of Different Snowstorm Types in Central New York State

Located at the eastern extent of the Great Lakes snowbelt, Central New York averages some of the highest annual snowfall totals east of the Rocky Mountains. This is in large part due to the variety of snowstorms that affect the region including lake-effect storms, coastal storms, and overrunning storms. Previous estimates suggest that lake-effect snowstorms account for approximately half of the seasonal snow in the Great Lakes basin, but ignore the spatial variability that exists within the region. Therefore, this study examines the seasonal snowfall contributions of the different snowstorm types to affect Central New York. Results suggest that although lake-effect snowstorms are the dominant snowstorm type in the region, their seasonal snowfall contributions vary between 13 and 48%. Although lake-effect snowstorms produce more snow during the peak and mid-seasons, their relative contribution is greatest during the early and mid-winter seasons. Generally, higher contributions occur near the Tug Hill Plateau, with lower contributions in southern Central New York. Instead, snowfall in southern Central New York is mostly dominated by Nor'easters (16–35%), with lesser contributions from Rocky lows (14–29%). Overrunning storms that originate in Canada (e.g., Alberta clippers) and non-cyclonic storms contribute the least to seasonal snowfall totals across Central New York; however, they are often the catalyst for lake-effect snowstorms in the region, as they advect continental polar air masses that destabilize across the lake. Understanding the actual snowfall contribution from different snowstorm types is needed for future climate predictions. Since the potential trajectory of future snowfall varies according to the type of storm, climate models must accurately predict the type of storm that is producing the snow.

Records of organic carbon isotopic composition (δ13Corg) and volcanism linked to changes in atmospheric pCO2 and climate during the Late Paleozoic Icehouse

The Late Paleozoic Icehouse (LPI) provides a deep-time perspective for climate-glaciation-environment coevolution and offers potential insights into future climatic and environmental predictions. Most previous studies attributed climatic and environmental changes during the LPI to perturbations of atmospheric p CO 2 , yet the driving mechanism for p CO 2 changes remains controversial. Although the environmental and climatic effects of volcanism have been recognized, its high-resolution geological records and links with changes in atmospheric p CO 2 and climate are rarely reported. We address this by investigating volcanic records (indicating by Hg/TOC ratio) and organic carbon isotope composition (δ 13 C org ) of paralic strata from the latest Gzhelian to Roadian stages in the North China Platform (NCP), China. Four δ 13 C org negative excursions (latest Gzhelian, middle Artinskian, middle Kungurian and latest Kungurian stages) and two positive isotope plateaus (Asselian and Roadian stages) are identified. Each of the four negative isotope excursions coincides with peaks in Hg/TOC ratio and rising periods of atmospheric p CO 2 . The co-occurrence of the negative isotope excursions and Hg/TOC peaks suggests volcanic origins for both the Hg and perturbations in the carbon cycle. The two positive isotope plateaus correspond to the glacial P1 and the climatic transition period to glacial P3 in Australia and to weak periods of volcanism. Our results provide clear insights into volcanism driving perturbations in environment, climate and the carbon cycle in deep time, and support the assertion that volcanism was sufficient to shift global climates from glacial to interglacial through greenhouse gas emissions during the LPI. • Early Permian continental δ 13 C org record presented from North China Platform. • Negative δ 13 C org excursions and Hg/TOC ratios identify episodes of volcanism. • Volcanism is important driver for changes in atmospheric p CO 2 and climate. • Two positive δ 13 C org plateaus correspond to P1 and P3 glaciations in Australia.

Climate change and timber in Latin America: Will the forestry sector flourish under climate change?

This paper examines the potential impact of climate change on forests and timber production in Latin America. The analysis links a Dynamic Global Vegetation Model (DGVM), which projects forest migration, forest dieback, and net primary productivity spatially as a function of future climate predictions, with a dynamic optimization model of global timber markets. A global model is used because climate change has impacts throughout the world, and it is important to capture these global interactions given the role of Latin American forests in global timber production. The resulting model projects timber investments, harvesting, and forest area in critical timber producing regions of Latin America, ranging from intensively managed forest plantations in Chile, Uruguay and Brazil, to less intensively managed tropical forests. The analysis is conducted with four climate models, two climate scenarios, one DGVM, and a single economic model. Without climate change, overall forest area is projected to decline in the region as forests continue to be converted to agricultural uses, although the area of plantations increases. Timber output increases as well, although the region modestly loses some market share in global timber markets in a couple scenarios. The land use trajectory for the region is not predicted to change significantly with climate change, however, total carbon storage does increase because forest productivity increases. Timber output also increases with climate change, but market share compared to the rest of the world is further diminished because slow-growing regions like Canada gain substantially from higher growth. There is substantial heterogeneity in the projected effects of climate change on plantation species across the region. In general, timber producers in the region are susceptible to growing competition from producers in other regions who may gain more from climate change.

Potential range shifts and climatic refugia of rupicolous reptiles in a biodiversity hotspot of South Africa

SummaryClimate change is causing the geographical ranges of some species to track suitable conditions. Habitat specialists, range-restricted species and species with limited dispersal abilities may be unable to track changing conditions, increasing their extinction risk. In response to changing conditions and species movement patterns, there is a need to account for the effects of climate change when designing protected areas and identifying potential climate refugia. We used ecological niche models projected into future climates to identify potential impacts of climate change on the distribution of 11 rupicolous reptile species in the Soutpansberg Mountains, South Africa.Lygodactylus incognitus, Lygodactylus soutpansbergensis, Platysaurus relictusandVhembelacerta rupicolawere identified as being vulnerable to climate change due to substantial reductions in suitable habitat and low spatial overlap between current and future niche envelopes. We identified areas of high conservation importance for the persistence of these species under present-day and projected future conditions. The western Soutpansberg was identified as an area of high conservation priority as it is a potential refuge under future projections. Projecting distributions of vulnerable species into future climate predictions can guide future research and identify potential refugia that will best conserve species with restricted ranges in a world with climate change.

Modeling landfill CH4 emissions

The current IPCC landfill methane (CH4) methodology excludes critical process drivers now known to control emissions. These include site-specific (1) operational factors (i.e., thickness and composition of various cover soils; physical extent of engineered biogas recovery) and (2) temporal climate effects on soil moisture/temperature profiles in each cover which, in turn, drive gaseous transport, microbial methanotrophic oxidation, and temporally variable “net” CH4 emissions over an annual cycle. Herein, we address the international field validation and application of a process-based model CAlifornia Landfill Methane Inventory Model (CALMIM) which encompasses site-specific climate, cover soils, engineered biogas recovery, and other site-specific strategies. Using embedded soil microclimate models with (a) default 30-year climate data, (b) site-specific annual weather data, or (c) future climate predictions (i.e., CMIP5), the transient soil moisture and temperature effects on bidirectional diffusive CH4/oxygen transport and microbial oxidation can be estimated for any cover soil at any global location. We focus on site-specific field data comparisons to CALMIM-predicted annual and monthly CH4 emissions both without and without methanotrophic oxidation. Overall, 74% of 168 individual surface CH4 emission measurements across 34 international sites were consistent with CALMIM-modeled annual predictions with oxidation (+ or – SD). Notably, the model overpredicted 30 comparisons and underpredicted 13 comparisons. In addition to improving site-specific landfill CH4 inventories, we address how this freely available tool can be used to (a) recommend site-specific cover soil modifications to minimize emissions; (b) systematically compare the spatial and temporal variability of emissions for diverse global locations, latitudinal gradients, extreme climates, and future climate scenarios; (c) assist scheduling of field campaigns to capture seasonal variability; and (d) provide a 12-month annual framework with average monthly CH4 emission statistics for comparison to periodic temporal results from diverse bottom-up and top-down field techniques with variable uncertainties. Importantly, CALMIM does not require intensive site-specific model calibrations.

Great lakes urban pocket-beach dynamics: A GIS-based analysis of infrastructure-design influences on geomorphic development

Chicago’s lakefront beaches experienced inundation and coastal erosion when Lake Michigan’s mean annual water level rose by >1.5 m between 2013 and 2019. Understanding beach geomorphic response to this type of lake-level event is important, as future climate predictions call for a continuation of decadal oscillatory patterns. A GIS-based study of beach change was conducted along the city’s urban lakefront, where sand is embayed by groins, jetties, and revetments. Morphologic changes associated with the most recent decadal lake-level rise were evaluated in context of the surrounding infrastructure. Beach morphometrics, derived from historical aerial images and available LiDAR products, were compared against the characteristics of the fixed urban infrastructure. Overwash volumes associated with an ∼1 m-rise in lake level scaled well with beach size (R2 = 0.88), suggesting that the creation of new sediment accommodation and its spatial distribution along the urban lakefront during lake-level rise is an important control on beach morphodynamic behavior. Overwash patterns were influenced by embayment orientation and groin characteristics. Counterclockwise beach rotation of up to 21° occurred in places where shorelines were exposed to the open lake. More tightly enclosed beaches retreated more uniformly along strike due to passive inundation of terrain with lake-level rise. Our insights provide managers with useful information on key beach behavioral patterns and how they are influenced by infrastructure design, allowing for the possibility of mitigation strategies to be emplaced in anticipation of future lake-level oscillations.

Ocean Aerobiology.

Ocean aerobiology is defined here as the study of biological particles of marine origin, including living organisms, present in the atmosphere and their role in ecological, biogeochemical, and climate processes. Hundreds of trillions of microorganisms are exchanged between ocean and atmosphere daily. Within a few days, tropospheric transport potentially disperses microorganisms over continents and between oceans. There is a need to better identify and quantify marine aerobiota, characterize the time spans and distances of marine microorganisms’ atmospheric transport, and determine whether microorganisms acclimate to atmospheric conditions and remain viable, or even grow. Exploring the atmosphere as a microbial habitat is fundamental for understanding the consequences of dispersal and will expand our knowledge of biodiversity, biogeography, and ecosystem connectivity across different marine environments. Marine organic matter is chemically transformed in the atmosphere, including remineralization back to CO2. The magnitude of these transformations is insignificant in the context of the annual marine carbon cycle, but may be a significant sink for marine recalcitrant organic matter over long (∼104 years) timescales. In addition, organic matter in sea spray aerosol plays a significant role in the Earth’s radiative budget by scattering solar radiation, and indirectly by affecting cloud properties. Marine organic matter is generally a poor source of cloud condensation nuclei (CCN), but a significant source of ice nucleating particles (INPs), affecting the formation of mixed-phase and ice clouds. This review will show that marine biogenic aerosol plays an impactful, but poorly constrained, role in marine ecosystems, biogeochemical processes, and the Earth’s climate system. Further work is needed to characterize the connectivity and feedbacks between the atmosphere and ocean ecosystems in order to integrate this complexity into Earth System models, facilitating future climate and biogeochemical predictions.

Characterization of chanterelle (Cantharellus cibarius) and pine mushrooms (Tricholoma magnivelare) in northern Saskatchewan

Baseline data on the boreal jack pine associated chanterelle (Cantharellus cibarius Fr.) and pine mushrooms (Tricholoma magnivelare (Peck) Redhead) in the Boreal Plain Ecozone of Saskatchewan were collected in five ecosites of productive mushroom areas. It investigated hourly weather parameters correlated with daily purchase volumes over 4 yrs; yield data over 5 yrs; and varying age, tree density, and species for presence of mushrooms in over 100 stands. All plots fell within the lichen jack pine (jP) ecosite with an overstory entirely of jack pine. The understory was dominated by reindeer lichen, bearberry, and blueberry. Plots were well- to rapidly drained, subxeric to submesic, with low nutrient regime. Both chanterelle and pine mushrooms were present in jack pine stands of < 20 yrs, with greatest occurrence in pure jack pine stands of 41–60 yrs in moderately open A-B canopy density. Weekly purchase data correlated with environmental parameters. Growing degree days (GDD) (base temperature 5 °C) + soil temperature (minimum 500 ± 70 GDD), + either soil moisture or precipitation (cumulative 50–100 mm) provided the highest regression value with chanterelle yield 6–13 wks prior to first appearance. The 5-yr total chanterelle yield from this region averaged 7100 kg·yr−1. Chanterelle cap diameter was a good predictor of fresh weight and proxy for yield. Pine mushroom was < 10% of chanterelle yield, averaging 1.72 kg·ha−1over 4 yrs based on 5 days picking. With future climate predictions of warmer and moister conditions, timing of fruiting body appearance is anticipated to advance.

Greening of the Qinghai–Tibet Plateau and Its Response to Climate Variations along Elevation Gradients

The vegetation of the Qinghai–Tibet Plateau (QTP) is vital to the global climate change and ecological security of China. However, the impact of climate variation on the spatial pattern and zonal distribution of vegetation in the QTP remains unclear. Accordingly, we used multisource remote-sensing vegetation indices (GIMMS-LAI, GIMMS NDVI, GLOBMAP LAI, MODIS EVI, MODIS NDVI, and MODIS NIRv), climate data, a digital elevation model, and the moving window method to investigate the changes in vegetation greenness and its response to climate variations in the QTP from 2001 to 2016. Results showed that the vegetation was greening in the QTP, which might be attributed to the increases in temperature and radiation. By contrast, the browning of vegetation may be caused by drought. Notably, the spatial patterns of vegetation greenness and its variations were linearly correlated with climate at low altitudes, and vegetation greenness was non-linearly correlated with climate at high altitudes. The Northwestern QTP needs to be focused on in regard to positive and decreased VGEG (vegetation greenness along the elevation gradient). The significantly positive VGEG was up to (34.37 ± 2.21) % of the QTP, which indicated a homogenization of vegetation greenness on elevation. This study will help us to understand the spatial distribution of vegetation greenness and VGEG in the QTP under global warming, and it will benefit ecological environment management, policymaking, and future climate and carbon sink (source) prediction.

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade‐off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade‐off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

Piecewise Evolutionary Spectra: A Practical Approach to Understanding Projected Changes in Spectral Relationships Between Circulation Modes and Regional Climate Under Global Warming

AbstractRegional climate variability is strongly related to large‐scale circulation modes. However, little is known about changes in their spectral characteristics under climate change. Here, we introduce piecewise evolutionary spectra to quantify time‐varying variability and co‐variability of climate variables, and use ensemble periodograms to estimate these spectra. By employing a large ensemble of climate change simulations, we show that changes in the variability and relationships of the North Atlantic Oscillation (NAO) and regional surface temperatures are disparate on individual timescales. The relation between NAO and surface temperature over high‐latitude lands weakens the most on 20‐year timescales compared to shorter timescales, whereas the relation between NAO and temperature over subtropical North Africa strengthens more on shorter timescales than on 20‐year timescales. These projected evolution and timescale‐dependent changes shed new light on the controlling factors of circulation‐induced regional changes. Accounting for them can lead to the improvement of future regional climate predictions.

Evaluation of Multi-Model Hindcasts of Overland Precipitation for Georgia

This study evaluates the ability of several Regional Climate Models (RCMs) to simulate rainfall patterns in the South Caucasus region. In total, 8 RCM simulations were assessed against the CRU observational database over different domains, among them two from the Coordinated Regional Climate Downscaling Experiment (CORDEX). Seasonal climatology, annual rainfall cycles and interannual variability in RCM outputs were estimated for 8 homogeneous sub-regions against several observational datasets. Different metrics covering from monthly and seasonal to annual time scales are analyzed over the region of interest. The results confirm the distinct capabilities of climate models in capturing the local features of the climatic conditions of the South Caucasus region. At the same time, the analysis shows significant deviations in individual models depending on the sub-region and season; however, the ensemble mean is in better agreement with observations than individual models. Overall, the analysis presented here demonstrates that, the multi-model ensemble mean adequately simulates rainfall in the South Caucasus and, therefore, it can be used to assess future climate predictions for the region. This work promotes the selection of RCM runs with reasonable performance in the South Caucasus region, from which, for the first time, a high-resolution bias-adjusted climate database can be generated for future risk assessment and impact studies.

Phyllosphere Community Assembly and Response to Drought Stress on Common Tropical and Temperate Forage Grasses.

Grasslands represent a critical ecosystem important for global food production, soil carbon storage, and water regulation. Current intensification and expansion practices add to the degradation of grasslands and dramatically increase greenhouse gas emissions and pollution. Thus, new ways to sustain and improve their productivity are needed. Research efforts focus on the plant-leaf microbiome, or phyllosphere, because its microbial members impact ecosystem function by influencing pathogen resistance, plant hormone production, and nutrient availability through processes including nitrogen fixation. However, little is known about grassland phyllospheres and their response to environmental stress. In this study, globally dominant temperate and tropical forage grass species were grown in a greenhouse under current climate conditions and drought conditions that mimic future climate predictions to understand if (i) plant host taxa influence microbial community assembly, (ii) microbial communities respond to drought stress, and (iii) phyllosphere community changes correlate to changes in plant host traits and stress-response strategies. Community analysis using high-resolution sequencing revealed Gammaproteobacteria as the dominant bacterial class, which increased under severe drought stress on both temperate and tropical grasses while overall bacterial community diversity declined. Bacterial community diversity, structure, and response to drought were significantly different between grass species. This community dependence on plant host species correlated with differences in grass species traits, which became more defined under drought stress conditions, suggesting symbiotic evolutionary relationships between plant hosts and their associated microbial community. Further understanding these strategies and the functions microbes provide to plants will help us utilize microbes to promote agricultural and ecosystem productivity in the future. IMPORTANCE Globally important grassland ecosystems are at risk of degradation due to poor management practices compounded by predicted increases in severity and duration of drought over the next century. Finding new ways to support grassland productivity is critical to maintaining their ecological and agricultural benefits. Discerning how grassland microbial communities change in response to climate stress will help us understand how plant-microbe relationships may be useful to sustainably support grasslands in the future. In this study, phyllosphere community diversity and composition were significantly altered under drought conditions. The significance of our research is demonstrating how severe climate stress reduces bacterial community diversity, which previously was directly associated with decreased plant productivity. These findings guide future questions about functional plant-microbe interactions under stress conditions, greatly enhancing our understanding of how bacteria can increase food security by promoting grassland growth and resilience.

Example of near-term decadal climate trend predictionbased on tree-ring data

<p indent=0mm>Accurately predicting future climate is the ultimate goal of palaeoclimate and climatology research, and it is also a hot topic and extremely difficult problem in the current climate field. In particular, the decadal climate prediction in the next <sc>10–30 years</sc> is of great reference value for government decision-making and medium- and long-term planning. However, up to now, there has been no case of near-term decadal climate prediction reported in the literature. The main problem lies in the slow development of long-term climate prediction theory, and the lack of sufficiently long, accurate, high-resolution, precise, and standardized climate proxy indicators. Tree rings have become an important proxy for the study of past climate change due to their high temporal resolution, accurate dating, wide geographical distribution, and strong continuity. Tree-ring-based reconstruction of climate factors (such as precipitation and temperature) over the past few hundred years has been carried out extensively in China. The tree-ring reconstruction series can reflect the local and regional climate factors in past long periods of time. In addition, the tree-ring reconstruction of climate series strictly follows the principles of data length, coherence, analogy, correlation, dynamics, and period required by statistical prediction. Therefore, by using a variety of mathematical analyses, we reconstructed the precipitation series from 1726 to 1997 based on the width of tree-ring latewood in the Mt. Helan region, and also used Caterpillar-Singular Spectrum Analysis (Caterpillar-SSA), a powerful time-series analysis and prediction method. Through the steps of nesting, singular value decomposition, aggregation, diagonal averaging, and extrapolation, the tree-ring time series in the Mt. Helan region were decomposed and the trend was extrapolated to forecast the precipitation trend from 1998 to 2022. The prediction results showed that the peak of precipitation in the Mt. Helan region occurred around 2002, and the precipitation will continue to decrease in the following <sc>10 years.</sc> The precipitation will reach its lowest point in 2013–2014, and will then increase slightly, but it cannot reach the average level of 1970–2002. Today, more than <sc>20 years</sc> later, actual weather observations can be used to objectively evaluate tree-ring prediction results, with the aim of exploring and improving appropriate near-term decadal climate prediction approaches. Results show that the aforementioned prediction result is close to the real precipitation trend in the subsequent <sc>20 years</sc> at several meteorological stations around the research site. The observation records support this prediction result, grasping the changing trend of first becoming wet, then rapidly drying, and then slowly becoming wet. The extreme drought events appearing during 2013–2014 in the prediction were also confirmed by the observations in the Mt. Helan region. The statistical prediction method of tree-ring reconstruction based on the internal relationship can not only give the continuous change trend, but can also predict the time nodes of extreme climate events. The method’s biggest advantage is that it does not need to rely on a mainframe computer for a long-term and large amount of data calculations. To date, to the best of our knowledge, this is the only published example of statistical prediction of the near-term decadal trend of climate change for <sc>20 years</sc> based on tree-ring data, which substantially advances the statistical prediction capabilities of tree-ring climate proxies. In future climate prediction, it is suggested to combine the numerical model prediction method based on dynamic equation solution with the tree-ring statistics method to obtain better prediction results and improve the ability of climate trend prediction. The combination of model and high-resolution reconstruction series will be an important way of improving the accuracy of climate prediction at present and into the future.

The role of Arctic gateways on sea ice and circulation in the Arctic and North Atlantic Oceans: a sensitivity study with an ocean-sea-ice model

The impact of changes in volume, heat and freshwater fluxes through Arctic gateways on sea ice, circulation and fresh water and heat contents of the Arctic and North Atlantic Oceans is not fully understood. To explore the role played by each gateway, we use a regional sea-ice ocean general circulation model with a fixed atmospheric forcing. We run sensitivity simulations with combinations of Bering Strait (BS) and Canadian Arctic Archipelago (CAA) open and closed inspired by paleogeography of the Arctic. We show that fluxes through BS influence the Arctic, Atlantic and Nordic Seas while the impact of the CAA is more dominant in the Nordic Seas. In the experiments with BS closed, there is a change in the surface circulation of the Arctic with a weakening of the Beaufort Gyre by about thirty percent. As a consequence, the Siberian river discharge is spread offshore to the west, rather than being directly advected away by the Transpolar Drift. This results in a decrease of salinity in the upper 50 m across much of the central Arctic and East Siberian and Chukchi Seas. We also find an increase in stratification between the surface and subsurface layers after closure of BS. Moreover, closure of the BS results in an upward shift of the relatively warm waters lying between 50 and 120 m, as well as a reorganization of heat storage and transport. Consequently, more heat is kept in the upper layers of the Arctic Ocean, thus increasing the heat content in the upper 50 m and leading to a thinner sea ice cover. The CAA closing has a large impact on sea ice, temperature and salinity in the subarctic North Atlantic with opposite responses in the Greenland-Iceland-Norwegian Seas and Baffin Bay. It is also found that CAA being open or closed strongly controls the sea ice export through the Fram Strait. In all our experiments, the changes in temperature and salinity of the Barents and Kara Seas, and in fluxes through Barents Sea Opening are relatively small, suggesting that they are likely controlled by the atmospheric processes. Our results demonstrate the need to take into consideration the fluxes through the Arctic gateways when addressing the ocean and climate changes during deglaciations as well as for predictions of future climate.

Assessment of Surface Water Availability under Climate Change Using Coupled SWAT-WEAP in Hongshui River Basin, China

Climate change adversely affects the hydrological cycle at the basin level. This study integrated two models, the Soil and Water Assessment Tool (SWAT) for future climate prediction, and Water Evaluation and Planning (WEAP) for the simulation of water quantity in the Hongshui River Basin (HRB), to evaluate the impacts of climate change, which plays a significant role in the lives of inhabitants downstream of the basin. Downscaled monthly rainfalls and temperatures under four Representative Concentration Pathways (RCPs) emission scenarios from five global circulation models (GCMs) were used to generate streamflow using the SWAT model. Streamflow data (1991–2001) were used to calibrate and validate, with the period of 1991–1997 used for calibration and that of 1998–2001 used for validation. Six scenarios were established to evaluate the response of the basin under socio-economic scenarios. The simulated results show that precipitation and streamflow would likely undergo a slight increase. The available water resources would be sufficient to meet the existing needs until 2050. The results indicated that no water shortages exist under socio-economic, low, and medium climate change emission scenarios, however the basin will experience a water shortage under the high climate change emission scenario (RCP-8.5). The study proposed that, to ensure the sustainability of water resources, better long-term management policies are required to be implemented in the basin and to meet future downstream water needs.

Sheep Excrement Increases Mass of Greenhouse Gases Emissions from Soil Growing Two Forage Crop and Multi-Cutting Reduces Intensity

To explore the effects of multi-cutting and sheep excrement on greenhouse gas (GHG) emissions from grassland ecosystems which simulate grazing livestock to a certain extent, spring wheat (Triticum aestivum L., var. Yongliang 15) and common vetch (Vicia sativa L., var. Lanjian 3) were planted in pot experiments in an inland arid region in 2019. Four treatments were conducted with eight replicates: plants without sheep excrement and cutting (CK), plants with multi-cutting (MC), plants with sheep excrement (SE), and plants with multi-cutting and sheep excrement (CE). The results showed that the carbon dioxide (CO2) emission of common vetch with CE significantly was higher than that with MC at the earlier and later branching stages (p &lt; 0.05). That of spring wheat with CE was significantly higher than that with MC at the later tillering stage (p &lt; 0.05). Nitrogen oxide (N2O) emissions of the two forage crops with SE rose significantly more than those with MC at both stages (p &lt; 0.05). Methane (CH4) of both forage crops with SE changed from absorption to emission (p &lt; 0.05). Soil NO3−-N content of both forages significantly increased with SE compared with MC (p &lt; 0.05), while soil NH4+-N content did not change significantly. Sheep excrement changed the CH4 sink into a CH4 source of the soil growing the two forage crops and increased the emissions of CO2 and N2O, whereas multi-cutting significantly reduced the GHG intensity of forage crops mostly by promoting the growth of the two forage crops. Future studies are suggested to identify the spatiotemporal effects of cutting and sheep excrement on GHG emissions to improve the prediction of future climate impacts from grazing activities.

Evaluation of CMIP5 Climate Models Using Historical Surface Air Temperatures in Central Asia

Using historical data compiled by the Climate Research Unit, spatial and temporal analysis, trend analysis, empirical orthogonal function (EOF) analysis, and Taylor diagram analysis were applied to test the ability of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to accurately simulate the annual mean surface air temperature in central Asia from the perspective of the average climate state and climate variability. Results show that each model can reasonably capture the spatial distribution characteristics of the surface air temperature in central Asia but cannot accurately describe the regional details of climate change impacts. Some of the studied models, including CNRM-CM5, GFDL-CM3, and GISS-E2-H, could better simulate the high- and low-value centers and the contour distribution of the surface air temperature. Taylor diagram analysis showed that the root mean square errors of all models were less than 3, the standard deviations were between 8.36 and 13.45, and the spatial correlation coefficients were greater than 0.96. EOF analysis showed that the multi-model ensemble can accurately reproduce the surface air temperature characteristics in central Asia from 1901 to 2005, including the rising periods and the fluctuations of the north and south inversion phases. Overall, this study provides a valuable reference for future climate prediction studies in central Asia.

Hydrological variation recorded in a subalpine peatland of Northeast Asia since the Little Ice Age and its possible driving mechanisms

Hydrological characteristics since the Little Ice Age (LIA) could provide a good reference for current climate analysis and future climate prediction. However, the hydrological variation since the LIA and its driving mechanisms in Northeast Asia remain unclear, which has severely restricted our understanding on the past, present and future hydroclimate changes in these regions. Here we reconstruct the hydrological dynamics over the past 700 years using samples from the Hani peatland a subalpine peatland of Changbai Mountains to reveal these issues. The analytical results from plant macrofossil and grain-size of the HN-1 core and the integrated moisture/precipitation records across the entire Northeast Asia indicate that the hydrological environments in Northeast Asia were wetter conditions during the period of 1300–1700 AD, dry conditions during the period of 1700–1850 AD, and wet conditions during the period of 1850–2018 AD, respectively. The possible driving mechanisms for the hydrological variations in Northeast Asia since the LIA can be divided into three models. La Niña-like conditions induced wetter conditions in Northeast Asia from 1300 to 1700 AD. From 1700 to 1850 AD, strong volcanic aerosol effects superimposed on weaker La Niña-like conditions, resulting in dry conditions in Northeast Asia. However, El Niño-like conditions induced wet conditions in Northeast Asia from 1850 to 2018 AD. These driving models suggest that the teleconnected influence of solar activity/sunspot could control the hydrological dynamics in Northeast Asia on a decadal-centennial scale through the ENSO activities and Walker Circulation changes since the LIA. Based on the periodicity relationship between hydrological conditions and sunspot, it can be predicted that the moisutre conditions in Northeast Asia would gradually decrease from 2030 to 2085 AD, and gradually increase from 2085 to 2140 AD.